Fact 23: Not all "cuneiform writing" actually meant something. Sometimes just imitating the general appearance of cuneiform-like signs could be enough — why bother to learn the whole complex writing system if others can't read it anyway?

The first ever world map was sketched thousands of years ago by Indian saint
“Ramanujacharya” who simply translated the following verse from Mahabharat and gave the world its real face

In Mahabharat,it is described how 'Maharishi Ved Vyasa' gave away his divine vision to Sanjay

Dhritarashtra's charioteer so that he could describe him the events of the upcoming war.

But, even before questions of war could begin, Dhritarashtra asked him to describe how the world looks like from space.

This is how he described the face of the world:

सुदर्शनं प्रवक्ष्यामि द्वीपं तु कुरुनन्दन। परिमण्डलो महाराज द्वीपोऽसौ चक्रसंस्थितः॥
यथा हि पुरुषः पश्येदादर्शे मुखमात्मनः। एवं सुदर्शनद्वीपो दृश्यते चन्द्रमण्डले॥ द्विरंशे पिप्पलस्तत्र द्विरंशे च शशो महान्।

—वेद व्यास, भीष्म पर्व, महाभारत

Meaning:-

हे कुरुनन्दन ! सुदर्शन नामक यह द्वीप चक्र की भाँति गोलाकार स्थित है, जैसे पुरुष दर्पण में अपना मुख देखता है, उसी प्रकार यह द्वीप चन्द्रमण्डल में दिखायी देता है। इसके दो अंशो मे पीपल और दो अंशो मे विशाल शश (खरगोश) दिखायी देता है।

Meaning: "Just like a man sees his face in the mirror, so does the Earth appears in the Universe. In the first part you see leaves of the Peepal Tree, and in the next part you see a Rabbit."

Based on this shloka, Saint Ramanujacharya sketched out the map, but the world laughed

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.

*Heads-up that this thread will feature some extreme examples of homophobia and transphobia.*

Hey folks, have you ever wondered why trans people face constant accusations of fetishism, sexual predation and child abuse/grooming? Well, today let's talk about stochastic terrorism.

So, first we have to talk about what stochastic terrorism actually is. Remember when there was a conspiracy that there was a pedophile ring operating out of a pizza parlour basement, and a crazed gunman showed up? It's that sort of incitement that stochastic terrorism describes.

It's the demonisation or incitement against a group of people or individuals with the intent that other, unaffiliated parties will act upon it; it's the releasing doctored footage and a shooter show up at Planned Parenthood as a direct result of what he's been led to believe.

So with that in mind, let's talk about Russia's extreme anti-LGBT laws that banned the "promotion" of LGBT lifestyles to children. A move that specifically marked LGBT people as a threat to children, and resulted in neo-nazis meting out vigilante "justice" against gay men.

Groups such as this would lure unsuspecting gay men via dating sites, and brutalise and humiliate them on camera. They did so under the guise of combating child abusers, one group calling themselves "Occupy